The U.S. Nuclear Regulatory Commission awarded two grants totaling $850,000 to boost nuclear engineering education, research, and workforce development at Rensselaer. The funding will support two new nuclear engineering professors as well as graduate student research. One of the awards, for $450,000, is a three-year grant for faculty development that will support the nuclear engineering research of two assistant professors in Rensselaer’s Department of Mechanical, Aerospace, and Nuclear Engineering. The second NRC grant, totaling $400,000 over the next four years, is designated for scholarships and fellowships that will help Rensselaer continue to attract the nation’s best and brightest nuclear engineering graduate students.

Assessing Emergency Response

A new six-year, $1.1 million grant from the U.S. Department of Homeland Security will allow Rensselaer researchers to investigate how different civil infrastructures within a city or countysuch as roadways, water and power utilities, hospitals, banks, or law enforcementinteract with each other and with the natural environment after a disaster. Using complex computer modeling to develop this “system of systems,” the researchers will create software that will allow infrastructure managers and emergency response organizations to better understand their interdependency and better coordinate their efforts, and in turn be better equipped and more prepared to respond to all types of disasters.

Study Helps Pinpoint Genetic Variations

An international team of researchers led by Petros Drineas, assistant professor of computer science, has identified just 200 positions within the curves of the DNA helix that they believe capture much of the genetic diversity in European Americans, a population with one of the most diverse and complex historic origins on Earth. Their findings narrow the search for the elusive ancestral clues known as single nucleotide polymorphisms that cause disease and account for the minute variations in the European American population. The research is the first to isolate genetic ancestral clues based on a method that is purely computational, requiring no previous personal history.